Search

The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot.ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future.iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species.iv. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.org/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.

Remotely sensed point clouds provide detailed structural data of landscapes and ecosystem characteristics. Especially in the analysis of forests and topography, this data type has proven its ability to derive relevant quantitative parameters such as biomass or subsidence rates. Arctic and boreal permafrost ecosystems are severely affected by climate change and resulting vegetation shifts, environmental disturbances, and permafrost thaw which lead to rapid changes in these northern environments that can be detected and characterized with point cloud datasets. In recent decades, the amount of point clouds acquired and generated in high-latitude regions by terrestrial (TLS), mobile (MLS), unmanned aerial system (UAS) based (ULS), up to airborne-based (ALS) LiDAR (Light detection and ranging) and Structure from Motion (SfM) has steadily increased. Multi-temporal datasets are available for a wide range of observation targets.The characteristics of the point clouds such as the extent of the area covered as well as the point density and thus the level of detail differ depending on the sensor, method, and the acquisition specifications. To use point cloud data for topographic, morphological, and forestry analysis, segmentation and classification of the point cloud into specific components such as individual trees, stems, foliage, or terrain features is essential. This is a time-consuming manual process and not feasible when addressing large datasets. Several previous analyses showed the potential for machine learning-based semantic segmentation of a single point cloud type, e.g., terrestrial LiDAR (TLS) with identical acquisition mode and sensor. We aim at an automated segmentation of different point cloud types generated by i) TLS, MLS, ULS and ALS as well as ii) SfM using (multi)spectral UAS and airborne image data to enable an analysis of Arctic and boreal permafrost ecosystems. Thereby, we will focus on the following questions:1) How can we reduce the time consuming process of labeling the point clouds?2) Can we train a model for segmentation using all point clouds or does transfer learning lead to better results?3) To what level of detail can we accurately segment and classify the different point cloud types?With this automated segmentation and classification, we aim to open up the possibility of exploiting the information contained in the multitude of point cloud data for a variety of ecological research applications.

Circumboreal forests represent close to 30% of all forested areas and are changing in response to climate, with potentially important feedback mechanisms to regional and global climate through altered carbon cycles and albedo dynamics (e.g., Loranty et al., 2018). A large portion of these boreal forests are located in Siberia, Russia. Here the forests are made up of mainly two types: evergreen (coniferous i.e., Pine, Picea) and summergreen (deciduous i.e., Larix) needle-leaf. The evergreen–summergreen forest zone stretching across Western Central Yakutia is a dynamic vegetation transition zone with high disturbance due to forest fire and potential invasion of evergreen forest taxa to the east into the summergreen dominated forest zone that needs mapping and monitoring.Sentinel-2 based Remote Sensing offers the opportunity to obtain forest type maps on a 10-20 m spatial scale. We provide in the SiDroForest (Siberian drone-mapped forest inventory) data collection (https://doi.org/10.1594/PANGAEA.933268), a Sentinel-2 data set containing Level-2 Bottom of Atmosphere labelled image patches for the early (April-May), peak (June-July) and late (August-September) summer seasons (van Geffen et al., 2022). This dataset contains 63 30 by 30-meter labelled patches with vegetation labels assigned derived from fieldwork measurements taken by the Alfred Wegener Institute in Siberia, Russia in 2018.Building on the SiDroForest dataset, we used K-means clustering to perform an unsupervised classification of Sentinel-2 for five locations from the SiDroForest set. We then assigned two broad forest classes in the Sentinel-2 images, summergreen and evergreen. We used the SiDroForest Sentinel-2 patches as validation data for the K-means generated classes in addition to the fieldwork and expert knowledge.The new dataset contains 100,000 labelled pixels, distributed over the two classes. We created the dataset for three time stamps to include different forest phenophases in the classification. The phenophases make it easier to distinguish between the two types of forests as summergreen’s spectral signal changes significantly over the seasons.We trained a Gaussian Naïve Bayes (GNB), a Random Forest (RF) and a Decision Tree (DT) classifier on three-time stamps separately. A combination of Sentinel-2 bands and the NDVI were evaluated with the different classifiers. The highest average accuracy score was achieved with a DT classifier and a balanced set for the two classes and the early summer time stamp and the NDVI band (82%). The peak summer also performed decently with 74%, but the accuracy dropped to 60% for the late summer time stamp. We used the trained DT to classify Sentinel-2 data at two locations in Siberia ; Lake Khamra and Nyurba. We masked out all non-forest data and created a forest map to measure the distribution of evergreen and summergreen over the larger areas. With our analyses we will improve the understanding of satellite data for monitoring remote places. The insights from the Siberian boreal forests are valuable in analyses of the boreal forests located in other parts of the world as well in these times or rapidly changing climate.

Boreal forests, which represent roughly one-third of the world’s total forested area, provide critical ecosystem services including carbon stocks, climate feedback, permafrost stability, biodiversity, and economic benefits. Located in the northern latitude, they are mainly dominated by evergreen needle-leaf tree taxa (Pinus, Picea, Abies) in North America, Northern Europe, and Western Siberia, and by deciduous needle-leaf tree taxa (Larix) in Eastern Siberia. Remote sensing applications in high latitudes are possible but remain challenging for optical satellite sensors due to frequent cloud coverage, forest fires, and low illumination. Additionally, there is little data available prepared as multi-label datasets for remote sensing applications focusing on the structure of boreal forests, specifically on Larix deciduous trees. Furthermore, labeled datasets of summer green and evergreen forest types for specific satellite sensors would enable remote sensing and deep learning applications such as classification, and ultimately improve our understanding of evergreen and summer green tree dynamics. An example of such a dataset is the TreeSatAI multi-sensor Artificial Intelligence Benchmark Archive (doi.org/10.5281/zenodo.6780578), which provides labels on species and forest composition in Europe. Another one is the SiDroForest data collection, consisting of a synthetic Unmanned Aerial Vehicle (UAV) Siberian Larch Dataset (doi.org/10.1594/PANGAEA.932795) and Sentinel-2 image patches (doi.org/10.1594/PANGAEA.933268) of 54 forest plots in Eastern Siberia. Here we are building up an extensive multi-labeled training dataset based on optical Sentinel-2 image patches (60 x 60 m image patch of the 10 m and 20 m S2-bands), including meta-data information on summer green and evergreen tree species and forest structure from vegetation plots. Over 250 vegetation plots were collected since 2011 from nine field expeditions of the Alfred Wegener Institute in Eastern Siberia (doi.org/10.5194/essd-14-5695-2022) and Western Canada, where vegetation was sampled and described, and UAV images were taken (UAV solely in 2021 and 2022). In addition to in-situ plots, we gathered all cloud-free Sentinel-2 data from late spring to early fall (May to October) that geographically coincides with the vegetation plots. Therefore, the dataset contains different phenophases of evergreen and summer green forests and provides detailed label information on forest structure – such as tree species and density. The multi-labeling will include broader and more detailed forest-type classes. Some examples of higher-level labels are “Sparse larch forest” or “Dense evergreen forest’’. The poster will demonstrate how we defined forest labels from in-situ data, UAV, Sentinel-2, and their corresponding spectral signatures.We anticipate our dataset to be a starting point for a significantly more extensive one with the addition of radar satellite sensors such as Sentinel-1 and TanDEM-X, and other ground vegetation plots (new expedition expected in Alaska and Canada in summer 2023), data search in literature and repositories– e.g. NASA Arctic Boreal Vulnerability Experiment. Our dataset will be publicly available and can be used as a training dataset for deep learning algorithms to identify and characterize evergreen and summer green needle-leaf trees in boreal forest regions.

We present a long-year data collection of freshwater chemistry from 590 high latitude lakes based on water sampling during our past expeditions in the Central and Eastern Siberian continuous permafrost regions. We compiled and standardised all acquired seasonal limnological data on major ions, alkalinity, pH and Electrical Conductivity including time series of data from lakes that we sampled repeatedly. The data collection encompasses diverse Siberian regions with numerous lakes along climate and vegetation gradients in the Khatanga and central Yakutian lowlands, to the glacial lakes of the Chukotka mountain region in Eastern Siberia and the central Lena Delta in the Arctic zone. The data collection is rich in standardised metadata following the metadata schema of the International Generic Sample Number (IGSN), which provides a unique and persistent identifier for physical objects (samples). Within the project FAIR Workflows to establish IGSN for Samples in the Helmholtz Association (FAIR WISH), funded by the Helmholtz Metadata Collaboration Platform (HMC), we customised the GFZ-specific IGSN schema to better describe lakes and water samples in a hierarchical (parent and child) scheme with standardised metadata on lake and sample characteristics, ecoregions, principal investigators and many more.This standardised data collection will be made available in the PANGAEA data repository (www.pangaea.de) to enable analyses of land-to-lake geochemical fluxes and will support biodiversity, biogeochemical, bioindicator and many more analyses. In this presentation, we visualize the data in form of Piper and Schoeller plots in order to categorize the water bodies and investigate their geographic distribution and links to catchment vegetation, active layer depth, permafrost disturbances and lithology.

The Lena Delta is the largest river delta in the Arctic (about 30 000 km2) and prone to rapid changes due to climate warming, associated cryosphere loss and ecological shifts. The delta is characterized by ice-rich permafrost landscapes and consists of geologically and geomorphologically diverse terraces covered with tundra vegetation and of active floodplains, featuring approximately 6 500 km of channels and over 30 000 lakes. Because of its broad landscape and habitat diversity the delta is a biodiversity hotspot with high numbers of nesting and breeding migratory birds, fish, caribou and other mammals and was designated a State Nature Reserve in 1995. Characterizing plant composition, above ground biomass and application of field spectroscopy was a major focus of a 2018 expedition to the delta. These field data collections were linked to Sentinel-2 satellite data to upscale local patterns in land cover and associated habitats to the entire delta. Here, we describe multiple field datasets collected in the Lena Delta during summer 2018 including foliage projective cover (Shevtsova et al., 2021a), above ground biomass (Shevtsova et al., 2021b), and hyperspectral field measurements (Runge et al., 2022, https://doi.pangaea.de/10.1594/PANGAEA.945982). We further describe a detailed Sentinel-2 satellite image-based classification of habitat types for the central Lena Delta (Landgraf et al., 2022), an upscaled classification for the entire Lena Delta (Lisovski et al., 2022), as well as a synthesis product for disturbance regimes (Heim and Lisovski, 2023, https://doi.org/10.5281/zenodo.7575691) in the delta that is based on the classification, the described datasets, and field expertise. We present context and detailed methods of these openly available datasets and show how they can improve our understanding of the rapidly changing Arctic tundra system. The new Lena Delta habitat distribution dataset represents a first baseline against which future observations can be compared. With the link between such detailed habitat type classifications and disturbance regimes future upscaling efforts may provide a better understanding of how Arctic lowland landscapes will respond to climate change and how this will impact land surface processes.

The International Generic Sample Number (IGSN) is a unique and persistent identifier for physical objects that was originally developed in the Geosciences. In 2022, after 10 years of service operation and more than 10 million registered samples worldwide, IGSN e.V. and DataCite have agreed on a strategic partnership. As a result, all IGSNs are now registered as DataCite DOIs and the IGSN metadata schema will be mapped to the DataCite Metadata Schema according to agreed guidelines. This will, on the one hand, enrich the very limited mandatory information shared by IGSN allocating agents so far. On the other hand, the DataCite metadata schema is not designed for the comprehensive description of physical objects and their provenance.The IGSN Metadata Schema is modular: the mandatory Registration Schema only included information on the IGSN identifier, the minting agent and a date - complemented by the IGSN Description Schema (for data discovery) and additional extensions by the allocating agents to customise the sample description according to their sample’s subdomain.Within the project “FAIR Workflows to establish IGSN for Samples in the Helmholtz Association (FAIR WISH)”, funded by the Helmholtz Metadata Collaboration Platform (HMC), we(1) customised the GFZ-specific schema to describe water, soil and vegetation samples and(2) support the metadata collection by the individual researcher with a user-friendly, easy-to-use batch registration template in MS Excel.The information collected with the template can directly be converted to XML files (or JSON in the future) following the IGSN Metadata schema that is required to generate IGSN landing pages. The template is also the source for the generation of DataCite metadata.The integration of linked data vocabularies (RDF, SKOS) in the metadata is an essential step in harmonising information across different research groups and institutions and important for the implementation of the FAIR Principles (Findable, Accessible, Interoperable, Reusable) for sample descriptions. More information on these controlled vocabularies can be found in the FAIR WISH D1 List of identified linked open data vocabularies to be included in IGSN metadata (https://doi.org/10.5281/zenodo.6787200).The template to register IGSNs for samples should ideally fit to various sample types. In a first step, we created templates for samples from surface water and vegetation from AWI polar expeditions on land (AWI Use Case) and incorporated the two other FAIR WISH use cases with core material from the Ketzin coring site (Ketzin Use Case) and for a wide range of marine biogeochemical samples (Hereon Use Case). The template comprises few mandatory and many optional variables to describe a sample, the sampling activity, location and so on. Users can easily create their Excel-template, including only the variables needed to describe a sample. A tutorial on how to use the FAIR WISH: Sample description template (https://doi.org/10.5281/zenodo.7520016) can be found in the FAIR WISH D3 Video Tutorial for the FAIR SAMPLES Template (https://doi.org/10.5281/zenodo.7381390). As our registration template is still a work in progress, we are furthermore happy for user feedback (https://doi.org/10.5281/zenodo.7377904).Here we will present the template and discuss its applicability for sample registration.

The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change. Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta. We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta. We extracted spring snow-cover duration determined by a latitudinal gradient. The ‘regular year’ snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta. We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed. In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021. The NDVI rise since 2018 is preceded by up to 4 °C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and processes that will provide urgently needed data in times when logistical challenges lead to data gaps in land-based observations in the rapidly changing Arctic.

Here we describe the LegacyPollen 1.0, a dataset of 2831 fossil pollen records with metadata, harmonized taxonomy, and standardized chronologies. A total of 1032 records originate from North America, 1075 from Europe, 488 from Asia, 150 from Latin America, 54 from Africa, and 32 from the Indo-Pacific. The pollen data cover the Late Quaternary (mostly the Holocene). The original 10,110 pollen taxa names (including variations in the notations) were harmonized to 1002 taxa, with woody taxa and major herbaceous taxa to genus level and other herbaceous taxa to family level. The dataset is valuable for synthesis studies such as taxa areal changes, vegetation dynamics, human impact (e.g., deforestation), and climate change at global or continental scales. The harmonized pollen and metadata as well as the harmonization table are available from PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.929773; Herzschuh et al., 2021a). R code for the harmonization is provided at Zenodo (https://doi.org/10.5281/zenodo.5910972; Herzschuh et al., 2022) so that datasets at a customized harmonization level can be easily established.

Modern pollen–vegetation–climate relationships underpin palaeovegetation and palaeoclimate reconstructions from fossil pollen records. East Siberia is an ideal area for investigating the relationships between modern pollen assemblages and near natural vegetation under cold continental climate conditions. Reliable pollen-based quantitative vegetation and climate reconstructions are still scarce due to the limited number of modern pollen datasets. Furthermore, differences in pollen representation of samples from lake sediments and soils are not well understood. Here, we present a new pollen dataset of 48 moss/soil and 24 lake surface-sediment samples collected in Chukotka and central Yakutia in East Siberia. The pollen–vegetation–climate relationships were investigated by ordination analyses. Generally, tundra and taiga vegetation types can be well distinguished in the surface pollen assemblages. Moss/soil and lake samples contain generally similar pollen assemblages as revealed by a Procrustes comparison with some exceptions. Overall, modern pollen assemblages reflect the temperature and precipitation gradients in the study areas as revealed by constrained ordination analysis. We estimate the relative pollen productivity (RPP) of major taxa and the relevant source area of pollen (RSAP) for moss/soil samples from Chukotka and central Yakutia using Extended R-Value (ERV) analysis. The RSAP of the tundra-forest transition area in Chukotka and taiga area in central Yakutia are ca. 1300 and 360 m, respectively. For Chukotka, RPPs relative to both Poaceae and Ericaceae were estimated while RPPs for central Yakutia were relative only to Ericaceae. Relative to Ericaceae (reference taxon, RPP = 1), Larix, Betula, Picea, and Pinus are overrepresented while Alnus, Cyperaceae, Poaceae, and Salix are underrepresented in the pollen spectra. Our estimates are in general agreement with previously published values and provide the basis for reliable quantitative reconstructions of East Siberian vegetation.

The International Geo Sample Number (IGSN) is a globally unique and persistent identifier (PID) for physical samples and collections with discovery function on the Internet. IGSNs enable to directly link data and publications with samples they originate from and thus close the last gap in the full provenance of research results. The modular IGSN metadata schema has a small number of mandatory and recommended metadata elements that can be individually extended with discipline-specific elements.Based on three use cases that represent all states of digitisation - from individual scientists, collecting sample descriptions in their field books to digital sample management systems fed by an app that is used in the field - FAIR WISH will (1) develop standardised and discipline specific IGSN metadata schemes for different sample types from the Earth and Environment Sciences, (2) develop workflows to generate machine-readable IGSN metadata from different states of digitisation, (3) develop workflows to automatically register IGSNs and (4) prepare the resulting workflows for further use in the Earth Science community.

This data collection is an attempt to remedy the scarcity of tree level forest structure data in the circum-boreal region, whilst providing, as part of the data collection, adjusted and labelled tree level and vegetation plot level data for machine learning and upscaling practices. Publicly available comprehensive datasets on tree level forest structure are rare, due to the involvement of governmental agencies, public sectors, and private actors that all influence the availability of these datasets. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest collection contains a variety of data mainly based on unmanned aerial vehicle (UAV) and field data collected from 64 vegetation plots during fieldwork jointly performed by the Alfred Wegener Institute for Polar and Marine Research (AWI) and the North-Eastern Federal University of Yakutsk (NEFU) during the Chukotka 2018 expedition to Siberia. The data collection consists of four separate datasets. The fieldwork locations are the anchors that bind the data types together based on the location of the vegetation plot. i) The first dataset (Kruse et al., 2021, https://doi.pangaea.de/10.1594/PANGAEA.933263) provides UAV-borne data products covering the 64 vegetation plots surveyed during fieldwork: including structure from motion (SfM) point clouds, point-cloud products such as Digital Elevation Model (DEM), Canopy Height Model (CHM), Digital Surface Model (DSM) and Digital Terrain Model (DTM) constructed from Red Green Blue (RGB) and Red Green Near Infrared (RGN) orthomosaics. Forest structure and vegetation composition data are crucial in the assessment of whether a forest is to act as a carbon sink under changing climate conditions. Fieldwork and UAV-products can provide such data in depth. ii) The second dataset contains spatial data in the form of points and polygon shape files of 872 labelled individual trees and shrubs that were recorded during fieldwork at the same vegetation plots with information on tree height, crown diameter, and species (van Geffen et al., 2021c, https://doi.pangaea.de/10.1594/PANGAEA.932821). These tree- and shrub-individual labelled point and polygon shape files were generated and are located on the UAV RGB orthoimages. The individual number links to the information collected during the expedition such as tree height, crown diameter and vitality provided in table format. This dataset can be used to link individual trees in the SfM point clouds, providing unique insights into the vegetation composition and also allows future monitoring of the individual trees and the contents of the recorded vegetation plots at large. iii) The third dataset contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.pangaea.de/10.1594/PANGAEA.932795). The synthetic dataset was created specifically to detect Siberian larch species. iv) If publicly available forest-structure datasets at tree level are rarely available for Siberia, even fewer ready-to-use tree and plot level data are available for machine learning approaches, for example optimised data formats containing annotated vegetation categories. The fourth set contains Sentinel-2 Level-2 bottom of atmosphere labelled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.pangaea.de/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to use validation and training data set to be used in various vegetation-related machine-learning tasks. The SidroForest data collection serves a variety of user communities. First of all, the UAV-derived top of canopy structure information, orthomosaics and the detailed vegetation information in the labelled data set provide detailed information on forest type, structure and composition for scientific communities with ecological and biological applications. The detailed Land Cover and Vegetation structure information in the first two data sets are of use for the generation and validation of Land Cover remote sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, parts of the SiDroForest dataset are prepared to be used as training and validation data for machine learning purposes. For example, the Synthetic tree crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest data set contains standardized Sentinel-2 labelled image patches that provide training data on vegetation class categories for machine learning classification with JSON labels provided. The SiDroForst data collective serves as a basis to add future data collected during expeditions performed by the Alfred Wegener Institute, creating a larger dataset in the upcoming years that can provide unique insights into remote hard to reach boreal regions of Siberia.

The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in Arctic nearshore zones and their role in the Carbon cycle are not well known due to difficult accessibility and challenging weather conditions. In this study we present the first multi-sensor turbidtiy- reflectance relationship that was specifically calibrated for Arctic nearshore environments. Field data was collected during summer seasons 2018 and 2019 in the inner shelf waters of the Canadian Beaufort Sea close to Herschel Island Qikiqtaruk. The turbidity-reflectance relationship was calibrated to mid to high spatial resolution sensors which are used in ocean color remote sensing, including Landsat 8, Sentinel 2, and Sentinel 3, using the relative spectral response functions. The results for Landsat 8 and Sentinel 2 are very promising and showcase the possibility to resolve sediment accumulations, sediment pathways and filaments at higher detail than before. Both sensors are able to resolve high turbidity close to the coast with values comparable to our field measurements. Sentinel 3, on the other hand, is too coarse to resolve these features but provides great applicability due to its high temporal resolution. The transferability of these relationships to nearshore environments outside the Canadian Beaufort Sea has to be tested in the future with the potential to map the sediment dispersal in nearshore environments at a circum- Arctic scale.

Vegetation biomass is a globally important climate-relevant terrestrial carbon pool. Landsat, Sentinel-2 and Sentinel-1 satellite missions provide a landscape-level opportunity to upscale tundra vegetation communities and biomass in high latitude terrestrial environments. We assessed the applicability of landscape-level remote sensing for the low Arctic Lena Delta region in Northern Yakutia, Siberia, Russia. The Lena Delta is the largest delta in the Arctic and is located North of the treeline and the 10 °C July isotherm at 72° Northern Latitude in the Laptev Sea region. We evaluated circum-Arctic harmonized ESA GlobPermafrost land cover and vegetation height remote sensing products covering subarctic to Arctic land cover types for the central Lena Delta. The products are freely available and published in the PANGAEA data repository under https://doi.org/10.1594/PANGAEA.897916, and https://doi.org/10.1594/PANGAEA.897045.Vegetation and biomass field data (30 m x 30 m plot size) and shrub samples for dendrology were collected during a Russian-German expedition in summer 2018 in the central Lena Delta. We also produced a regionally optimized land cover classification for the central Lena Delta based on the in-situ vegetation data and a summer 2018 Sentinel-2 acquisition that we optimized on the biomass and wetness regimes. We also produced biomass maps derived from Sentinel-2 at a pixel size of 20 m investigating several techniques. The final biomass product for the central Lena Delta shows realistic spatial patterns of biomass distribution, and also showing smaller scale patterns. However, patches of high shrubs in the tundra landscape could not spatially be resolved by all of the landscape-level land cover and biomass remote sensing products.Biomass is providing the magnitude of the carbon flux, whereas stand age is irreplaceable to provide the cycle rate. We found that high disturbance regimes such as floodplains, valleys, and other areas of thermo-erosion are linked to high and rapid above ground carbon fluxes compared to low disturbance on Yedoma upland tundra and Holocene terraces with decades slower and in magnitude smaller above ground carbon fluxes.

Changes in future above-ground biomass (AGB) of terrestrial ecosystems is one of the current interests in the light of climate change and essential to forecast for predicting potential climate feedbacks such as influence on the carbon balance. The tundra-taiga ecotone is a region that is prone to notable above-ground biomass changes, in the first instance due to the treeline advance. Forest is expected to occupy non-polygonal tundra. Our study region in central Chukotka (Northeastern Siberia) is a mountainous area on the northern border of the tundra-taiga ecotone that covers a wide range of vegetation types on a density gradient starting with lichen communities via open graminoid tundra to forest tundra. There is only one tree species – a deciduous conifer Larix cajanderi. We applied the individual-based spatially explicit model LAVESI to simulate larch AGB change from nowadays to 3000 AD under different climate scenarios, depending on Representative Concentration Pathways (RCPs) RCP 2.6, RCP 4.5 and RCP 8.5. We implemented in the model topographical parameters, as well as region-specific individual larch AGB equations, biological parameters of the tree growth and climate variables. We validated the new version of the model against field and Landsat satellite-based data, as well as a high spatial resolution image with distinctive trees visible, provided by ESRI (ArcGIS/World_imagery). Our first results are indicating mostly densification of existing tree stands before 2200 AD and forest expansion in the study region after 2200 AD even under the mildest RCP 2.6 scenario. First evaluations of the average tree AGB increase rates from present to 2200 AD are ranges from 0.007 (RCP 2.6) to 0.01 (RCP 8.5) kg*m-2*yr-1. Obtained rates of tree AGB change and its future distribution on the landscape can be particularly useful for conservation measures and modelling of future above-ground carbon stock dynamics.

To gain a better understanding of global carbon storage and albedo feedback mechanisms it is important to have insights into high latitude vegetation change. Boreal forest compositions are changing in response to changes in climate, which in turn can lead to feedbacks in regional and global climate through altered carbon cycles and albedo dynamics. Circumpolar boreal forests represent close to 30% of all forested area on the planet, between 900 and 1,200 million ha. These forests are located primarily in Alaska, Canada, and Russia. Due to the remote location of these forests and the short seasons without snow, data collected on the boreal vegetation is limited. The proposed dataset is an attempt to remedy data scarcity whilst providing adjusted data for machine learning practices.We present a dataset containing diverse formats of forest structure information that covers two important vegetation transition zones in Siberia: the Evergreen - Summergreen transition zone in Central Yakutia and the northern treeline in Chukotka (NE Siberia).This dataset contains data from the locations covered by fieldwork was performed by the Alfred Wegener Institute for Polar and Marine research, (AWI) and the North-Eastern Federal University of Yakutsk​ (NEFU). The fieldwork upscaled through the addition of Red Green Blue(RGB) UAV (Unmanned Aerial Vehicle) camera data and Sentinel-2 satellite data cropped to a 5 km radius around the fieldwork sites. The dataset is created with the aim of providing ground truth validation and training data to be used in various vegetation related machine learning tasks .The dataset contains:1.Labelled individual trees per 30x30 m plot assigned in field work with additional data on species, height, crown width, and biomass.2.Structure from Motion (SfM)point clouds that provide 3D information about the forest structure, included generated Canopy Height Model (CHM), Digital Elevation Model (DEM) and a Digital Surface Model (DSM) per 50x50 m.3.Multispectral Sentinel-2 satellite data (10 m ) cropped to a 5km radius with generated a NDVI(normalized difference vegetation index), available in three seasons: Early Summer, Peak Summer and Late Summer.4.Extracted tree crowns with species information and a synthetically generated large (10.000 samples) dataset for training machine leaning algorithms.The dataset will be made publicly available on the data repository PANGAEA.

Remobilization of soil carbon as a result of permafrost degradation in the drainage basin of the major Siberian rivers combined with higher precipitation in a warming climate potentially increase the flux of terrestrial derived dissolved organic matter (tDOM) into the Arctic Ocean. The Laptev (LS) and East Siberian Seas (ESS) receive enormous amounts of tDOM-rich river water, which undergoes at least one freeze-melt cycle in the Siberian Arctic shelf seas. To better understand how freezing and melting affect the tDOM dynamics in the LS and ESS, we sampled sea ice, river and seawater for their dissolved organic carbon (DOC) concentration and the colored fraction of dissolved organic matter. The sampling took place in different seasons over a period of 9 years (2010–2019). Our results suggest that the main factor regulating the tDOM distribution in the LS and ESS is the mixing of marine waters with freshwater sources carrying different tDOM concentrations. Of particular importance in this context are the 211 km3 of meltwater from land-fast ice from the LS, containing ~ 0.3 Tg DOC, which in spring mixes with 245 km3 of river water from the peak spring discharge of the Lena River, carrying ~ 2.4 Tg DOC into the LS. During the ice-free season, tDOM transport on the shelves takes place in the surface mixed layer, with the direction of transport depending on the prevailing wind direction. In winter, about 1.2 Tg of brine-related DOC, which was expelled from the growing land-fast ice in the LS, is transported in the near-surface water layer into the Transpolar Drift Stream that flows from the Siberian Shelf toward Greenland. The actual water depth in which the tDOM-rich brines are transported, depends mainly on the density stratification of the LS and ESS in the preceding summer and the amount of ice produced in winter. We suspect that climate change in the Arctic will fundamentally alter the dynamics of tDOM transport in the Arctic marginal seas, which will also have consequences for the Arctic carbon cycle.

Lakes in permafrost regions are dynamic landscape components and play an important role for climate change feedbacks. Lake processes such as mineralization and flocculation of dissolved organic carbon (DOC), one of the main carbon fractions in lakes, contribute to the greenhouse effect and are part of the global carbon cycle. These processes are in focus of climate research but studies so far are limited to specific study regions. In our synthesis, we analysed 2,167 water samples from 1,833 lakes across the Arctic in permafrost regions of Alaska, Canada, Greenland, and Siberia to provide first pan-Arctic insights for linkages between DOC concentrations and the environment. Using published data and unpublished datasets from the author team we report regional DOC differences linked to latitude, permafrost zones, ecoregions, geology, near-surface soil organic carbon contents, and ground ice classification of each lake region. The lake DOC concentrations in our dataset range from 0 mg L−1 to 1,130 mg L−1 (10.8 mg L−1 median DOC concentration). Regarding the permafrost regions of our synthesis, we found median lake DOC concentrations of 12.4 mg L−1 (Siberia), 12.3 mg L−1 (Alaska), 10.3 mg L−1 (Greenland), and 4.5 mg L−1 (Canada). Our synthesis shows a significant relationship of lake DOC concentration and ecoregion of the lake. We found higher lake DOC concentrations in boreal permafrost sites compared to tundra sites. About 22 % of the lakes in our extensive dataset are located in regions with ice-rich syngenetic permafrost deposits (yedoma). Yedoma contains large amounts of easily erodible organic carbon and we found significantly higher DOC concentrations in yedoma lakes compared to non-yedoma lakes. Compared to previous studies we found a weak significant relationship of soil organic carbon content and lake DOC concentration as well as between ground-ice content and lake DOC. Our pan-Arctic dataset shows that the DOC concentration of a lake strongly depends on its environmental properties, especially on permafrost extent and ecoregion, as well as vegetation, which is the most important driver of lake DOC in this study. This new dataset will be fundamental to quantify a pan-Arctic lake DOC pool for estimations of the impact of lake DOC on the global carbon cycle and climate change.

Permafrost-region lakes are dynamic landscape systems and play an important role for climate change feedbacks. Lake processes such as mineralization and flocculation of DOC, one of the main carbon fraction in lakes, contribute to the global carbon cycle. These processes are in focus of climate research but studies have been limited in geographic extent. We synthesized published datasets and unpublished datasets from the author team totaling 1,691 water samples from 1,387 lakes across the Subarctic and Arctic in permafrost regions of Alaska, Canada, Siberia, and Greenland to provide first insights for linkages between DOC concentration to the basin. In our synthesis, we find regional differences in DOC concentration of permafrost-region lakes. We focussed on relations between lake DOC concentration and latitude, permafrost zones, ecoregions, lake surrounding deposit type, and ground ice classification of each lake basin. Additionally, we analysed the lake surrounding soil organic carbon content from 0-100 cm depth and 0-300 cm depth. Individual lake DOC concentrations of our dataset range from below detection limit assigned to 0 mg L-1 (North Slope, Alaska) to 1,130 mg L-1 (Yukon Flats, Alaska). We found regional median lake DOC concentrations of 18.8 mg L-1 (Greenland, n=25), 12.2 mg L-1 (Alaska, n= 1,135), 9.6 mg L-1 (Siberia, n=252), and 7.2 mg L-1 (Canada, n=279). Lakes in the isolated permafrost zone had the highest median DOC concentration compared to lakes in the sporadic, discontinuous, and continuous permafrost zones. Our synthesis shows increasing lake DOC concentration with decreasing latitude and, due to a larger availability of biomass and organic carbon, a significant relationship of lake DOC concentration and ecoregion of the lake. We found higher lake DOC concentrations in boreal permafrost sites compared to tundra sites. About 22 % of lakes in our dataset are located in regions with ice-rich syngenetic permafrost deposits (yedoma). Because yedoma contains large amounts of organic carbon, we assumed to find higher DOC concentrations in yedoma lakes compared to non-yedoma lakes. Our analysis shows a significant relationship of lake DOC concentration and surrounding deposit type but not a higher DOC concentration in yedoma lakes compared to non-yedoma lakes. Finally, we found a relationship of soil organic carbon content from 0-100 cm depth and lake DOC concentration. In contrast, a comparison of soil organic carbon content from 0-300 cm depth and lake DOC concentration shows no significant correlation. This was also found for ground-ice content and lake DOC concentration. Our dataset of lakes across the Arctic shows that the DOC concentration of a lake strongly depends on its environmental properties. This dataset will be fundamental to establish a pan-Arctic lake DOC pool for estimations of the impact of lake DOC on the global carbon cycle and further on climate change.

Climate change is especially prominent in Arctic and sub-Arctic regions. Therefore, it is of great importance to investigate ecosystem processes and the dynamics of its components in the Siberian high latitudes. Plant biomass assessment is essential for estimating carbon stocks and further carbon balance and wildlife habitat modeling. Our study region in central Chukotka (north-eastern Siberia) is one of the least investigated sub-Arctic regions in terms of vegetation dynamics. We quantified changes in four vegetation classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra, and, (4) prostrate herb tundra and barren areas. We used Landsat spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Snow Index (NDSI)) to map the vegetation classes in four focus areas in 2000/2001 and 2016/2017. In 2016, we collected field data on foliage projective cover (percentage cover) of dominant taxa from 52 sites along the tundra–taiga gradient. We applied constrained ordination for coupling projective cover with corresponding Landsat spectral indices from 2016/2017. Ordination scores were used in a k-means classification. We inferred significant shrubification in the tundra–taiga zone (20%) and in the northern taiga (40%), as well as notable tree infilling in the northern taiga (9%), and, no significant changes in the treeless tundra area. To estimate carbon stocks and its changes within and between differentiated vegetation classes, we aim to upscale above ground biomass across tundra, tundra-taiga and northern taiga zones and derive above ground biomass change for the 15 investigated years. In 2018, another expedition took part to this region with new 38 sites at witch we described projective cover and representatively harvested total above ground biomass (dominant taxa and rest). This data can be projected in the created previously ordination space with the use of projective cover similarities. Using interpolation, we plan to predict above ground biomass in the whole ordination space. We will further use Landsat data and interpolation results to produce above ground biomass maps for the four focus areas in central Chukotka and derive difference maps from 2000/2001 to 2016/2017.

The carbon export by rivers to the Arctic Ocean is expected to increase in response to the rapidly changing climate in the Arctic (Camill, 2005; Freeman et al., 2001; Frey and Smith, 2005). This is in part due to thawing permafrost and mobilization of particulate and dissolved organic matter (DOM). The Lena River delivers approximately one fifth of the total river discharge to the Arctic Ocean and is the main source of DOM in the Laptev Sea shelf (Thibodeau et al., 2014). To date river fluxes of DOM have been based on sparse coverage of sample across the hydrograph about 700 km upstream (Cooper et al 2005; Raymond et al 2007; Stedmon et al 2011; Amon et al 2012). The effects of low frequency sampling on load estimates are unknown and potentially large for systems such as these where there are considerable changes across the hydrograph. Here we present results from a unique high frequency sampling program and evaluate its viability to monitor export fluxes of DOM and its biogeochemistry in the Lena River. The sampling takes place close to the river mouth at the research station Samoylov in the central Lena River Delta. The Samoylov research station allows a unique chance for continuous sampling since it operates throughout the year. The sampling program includes measurements of several water parameters, such as temperature, electric conductivity, dissolved organic carbon (DOC), spectral CDOM absorption (aCDOM), fluorescent dissolved organic matter (FDOM) and water stable isotopes.The data facilitated the identification of the main drivers behind the seasonality of DOM concentration and biogeochemistry of the Lena River. Three main water sources could be identified (1) (snow) melt water, (2) rain water and (3) subsurface water. Melt and rain water are found to be the prevailing water sources that combined transport 5.8 Tg C dissolved organic matter (~ 85 % of annual flux (6.8 Tg C)) into the Lena River. The high number of samples throughout the whole year allowed flux calculations that are independently from load models that likely lead to a large variation of earlier studies.The absorption properties of DOM revealed changing composition and sources of DOM throughout the year. Decreasing SUVA values during the summer point towards an increasing fraction of old DOM which potentially originates from degrading permafrost. In contrast, during the spring freshet, high SUVA indicate mostly fresh organic matter with high molecular weight and high aromaticity.This dataset represents the first year of a planned long-term monitoring program at the Research Station Samoylov Island and provides a baseline data set against which future change of this large integrative system may be measured. A continuous sampling of Arctic River water will facilitate to identify intra and inter-annual trends with ongoing climate change.

Large scale analyses of climatic or ecological data are important to understand complex relationships. Often, such data are available in open repositories or national measurement programmes, others are only made available via the responsible researcher. However, merging data from various sources is often not straightforward, due to issues with the data itself or the metadata. Nevertheless, the application of such compilations offers various possibilities. In our working group, two large-scale compilations are currently constructed and applied. The Northern Hemispheric Pollen Compilation consists of data from NEOTOMA, European Pollen Database (EPD), PANGAEA and various authors. With the help of this compilation, we reconstruct climate and vegetation of large spatial and temporal scales. The circumpolar soil temperature dataset consist of data from the Global Terrestrial Network for Permafrost (GTN-P), Roshydromet, PANGAEA, Nordicana D and the National Science Foundation (NSF) Arctic Data Center. In its first version, the compilation has already been successfully applied to validate the ESA CCI Permafrost soil temperature map.The various sources of errors and problems will be shown by the two compilations of (i) sedimentary pollen data and (ii) soil temperature data. The most general problem and error source are wrong or inaccurate coordinates. These errors arise out of coordinates provided with two decimals only, wrong conversion of DMS to decimal format, wrong coordinates etc. For most analyses, the most exact geographic position is a prerequisite, as e.g. lake size is an important parameter when reconstructing vegetation out of sedimentary pollen data. Sedimentary pollen records not located in a lake according to their given location thus need manual reposition according to the main researcher of a dataset or satellite maps. Further challenges concerning the pollen dataset pose various naming conventions or variable resolution in time. Furthermore, taxonomic resolution varies between datasets, making homogenization necessary.But also for the soil temperature dataset, extensive checks were necessary, as even quality checked data comprise erroneous values. Furthermore, measured depths vary between datasets. For easy comparisons of soil temperature simulations against data, standardized depths were extracted. In a future step, interpolations between measured depths will help the end-users to extract the exactly needed depths and a compilation of available metadata on e.g. surrounding vegetation and borehole stratigraphy shall be provided.All compilations will be made available on public repositories.

The intellectual disability gene, Sox11, encodes for a critical neurodevelopmental transcription factor with functions in precursor survival, neuronal fate determination, migration and morphogenesis. The mechanisms regulating SOX11’s activity remain largely unknown. Mass spectrometric analysis uncovered that SOX11 can be post-translationally modified by phosphorylation. Here, we report that phosphorylatable serines surrounding the high-mobility group box modulate SOX11’s transcriptional activity. Through Mass Spectrometry (MS), co-immunoprecipitation assays and in vitro phosphorylation assays followed by MS we verified that protein kinase A (PKA) interacts with SOX11 and phosphorylates it on S133. In vivo replacement of SoxC factors in developing adult-generated hippocampal neurons with SOX11 S133 phospho-mutants indicated that phosphorylation on S133 modulates dendrite development of adult-born dentate granule neurons, while reporter assays suggested that S133 phosphorylation fine-tunes the activation of select target genes. These data provide novel insight into the control of the critical neurodevelopmental regulator SOX11 and imply SOX11 as a mediator of PKA-regulated neuronal development.

Vegetation is responding to climate change, which is especially prominent in the Arctic. Vegetation change is manifest in different ways and varies regionally, depending on the characteristics of the investigated area. Although vegetation in some Arctic areas has been thoroughly investigated, central Chukotka (NE Siberia) with its highly diverse vegetation, mountainous landscape and deciduous needle-leaf treeline remains poorly explored, despite showing strong greening in remote-sensing products. Here we quantify recent vegetation compositional changes in central Chukotka over 15 years between 2000/2001/2002 and 2016/2017. We numerically related field-derived information on foliage projective cover (percentage cover) of different plant taxa from 52 vegetation plots to remote-sensing derived (Landsat) spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Snow Index (NDSI)) using constrained ordination. Clustering of ordination scores resulted in four land-cover classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra and (4) prostrate herb tundra and barren areas. We produced land-cover maps for early (2000, 2001 or 2002) and recent (2016 or 2017) time-slices for four focus regions along the tundra-taiga vegetation gradient. Transition from graminoid tundra to forest tundra and shrub tundra is interpreted as shrubification and amounts to 20% area increase in the tundra-taiga zone and 40% area increase in the northern taiga. Major contributors of shrubification are alder, dwarf birch and some species of the heather family. Land-cover change from the forest tundra and shrub tundra class to the larch closed-canopy forest class is interpreted as tree infilling and is notable in the northern taiga. We find almost no land-cover changes in the present treeless tundra.

International audience; The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in the nearshore zone are not well known, because ships do not often reach shallow waters and satellite remote sensing is traditionally focused on less dynamic environments. The goal of this study is to use the extensive Landsat archive to investigate sediment dispersal patterns specifically on an exemplary Arctic nearshore environment, where field measurements are often scarce. Multiple Landsat scenes were combined to calculate means of sediment dispersal and sea surface temperature under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use observations in the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to turbidity and sea surface temperature. We map the spatial patterns of turbidity and water temperature at high spatial resolution in order to resolve transport pathways of water and sediment at the water surface. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, both turbidity and water temperature are significantly higher in the nearshore area. The extent of the Mackenzie River plume and coastal erosion are the main explanatory variables for sediment dispersal and sea surface temperature distributions in the study area. During northwesterly wind conditions, the influence of the Mackenzie River plume is negligible. Our results highlight the potential of high spatial resolution Landsat imagery to detect small-scale hydrodynamic processes, but also show the need to specifically tune optical models for Arctic nearshore environments.

This paper describes two gas‐emission craters (GECs) in permafrost regions of the Yamal and Gydan peninsulas. We show that in three consecutive years after GEC formation (2014–2017), both morphometry and hydrochemistry of the inner crater lakes can become indistinguishable from other lakes. Craters GEC‐1 and AntGEC, with initial depths of 50–70 and 15–19 m respectively, have transformed into lakes 3–5 m deep. Crater‐like depressions were mapped in the bottom of 13 out of 22 Yamal lakes. However, we found no evidence that these depressions could have been formed as a result of gas emission. Dissolved methane (dCH4) concentration measured in the water collected from these depressions was at a background level (45 ppm on average). Yet, the concentration of dCH4 from the near‐bottom layer of lake GEC‐1 was significantly higher (824–968 ppm) during initial stages. We established that hydrochemical parameters (dissolved organic carbon, major ions, isotopes) measured in GEC lakes approached values measured in other lakes over time. Therefore, these parameters could not be used to search for Western Siberian lakes that potentially resulted from gas emission. Temperature profiles measured in GEC lakes show that the water column temperatures in GEC‐1 are lower than in Yamal lakes and in AntGEC – close to values of Gydan lakes. Given the initial GEC depth > 50 m, we suggest that at least in GEC‐1 possible re‐freezing of sediments from below might take place. However, with the present data we cannot establish the modern thickness of the closed talik under newly formed GEC lakes.

The presence of optically active water constituents is known to attenuate the light penetration in the ocean and impact the ocean heat content. Here, we investigate the influence of coloured dissolved organic matter (CDOM) and total suspended matter (TSM) on the radiative heating of the Laptev Sea shelf waters. The Laptev Sea region is heavily influenced by the Lena River, one of the largest river systems in the Arctic region. We simulate the radiative heating by using a coupled atmosphere-ocean radiative transfer model (RTM) and \textit{in situ} measurements from the TRANSDRIFT XVII expedition carried out in September 2010. The results indicate that CDOM and TSM have significant influence on the energy budget of the Laptev Sea shelf waters, absorbing most of the solar energy in the first \replaced{two meters}{2 m} of the water column. In the station with the highest CDOM absorption (a\textsubscript{CDOM}(443) = 1.77 m\textsuperscript{-1}) \replaced{$\sim$43}{42.6}\% more energy is absorbed in the surface layer compared to the station with the lowest a\textsubscript{CDOM}(443) ($\sim$0.2 m\textsuperscript{-1}), which translates to an increased \replaced{radiative}{radiant} heating rate of \replaced{$\sim$0.6}{0.57}\textdegree{}C\deleted[remark=]{/day}. The increased absorbed energy by the water constituents also implies in increased sea ice melt rate and changes in the surface heat fluxes to the atmosphere. By \replaced{using}{combining} satellite remote sensing and RTM we \replaced{quantify}{present} the spatial distribution of the radiative heating in the Laptev Sea for a typical summer day. The tools developed here \added[remark=]{(the combined use of satellite remote sensing, RT modeling and \textit{in situ} observations)} and tested in our case study can be used to improve the parameterizations of coupled atmosphere-ocean models to assess the \replaced{role}{feedback} of the ocean \added[remark=]{in the effect of} Arctic \replaced{a}{A}mplification.

Displacements of the Earth's surface can be estimated using differential interferometric synthetic aperture radar. The estimates are derived from the phase difference between two radar acquisitions. When at least three such acquisitions are available, one can compute the displacement between the first and the third acquisition and compare it with the sum of the two intermediate displacements. These two are expected to be equal for a piston-like spatially uniform deformation. However, this is not necessarily the case in measured data. Such lack of phase closure can be due to decorrelation noise alone. It has also been attributed to complex scattering processes such as soil moisture changes or multiple scattering sources. However, the nature of these nonrandom effects is only poorly understood in cold regions, as the role of snow and freeze/thaw processes has not been studied to date. To distinguish the noise-like and the systematic effects, an asymptotic Wald significance test is proposed. It detects situations when the observed closure error cannot solely be explained by noise. Such situations with $p are observed at the Ku-band during snow metamorphism and melt and following a summer precipitation event in Sodankyla, Finland. They can also be prevalent (> 25%) in the X-band observations of ice-rich permafrost regions in the Lena Delta, Russia, indicating the presence of processes that can have systematic and deleterious impacts on the estimation of surface movements. Satellite-based monitoring of these displacements is thus possibly subject to complex error sources in high-latitude regions.

The focus of this research has been on detecting changes in lake areas, vegetation, land surface temperatures, and the area covered by snow, using data from remote sensing. The study area covers the main (central) part of the Lena River catchment in the Yakutia region of Siberia (Russia), extending from east of Yakutsk to the central Siberian Plateau, and from the southern Lena River to north of the Vilyui River. Approximately 90% of the area is underlain by continuous permafrost. Remote sensing products were used to analyze changes in water bodies, land surface temperature (LST), and leaf area index (LAI), as well as the occurrence and extent of forest fires, and the area and duration of snow cover. The remote sensing analyses (for LST, snow cover, LAI, and fire) were based on MODIS-derived NASA products (250-1000 m) for 2000 to 2011. Changes in water bodies were calculated from two mosaics of (USGS) Landsat (30 m) satellite images from 2002 and 2009. Within the study area's 315,000 km2 the total area covered by lakes increased by 17.9% between 2002 and 2009, but this increase varied in different parts of the study area, ranging between 11% and 42%.The land surface temperatures showed a consistent warming trend, with an average increase of about 0.12 °C/year. The average rate of warming during the April-May transition period was 0.17 °C/year and 0.19 °C/year in the September-October period, but ranged up to 0.49 °C/year during September-October. Regional differences in the rates of land surface temperature change, and possible reasons for the temperature changes, are discussed with respect to changes in the land cover.Our analysis of a broad spectrum of variables over the study area suggests that the spring warming trend is very likely to be due to changes in the area covered by snow. The warming trend observed in fall does not, however, appear to be directly related to any changes in the area of snow cover, or to the atmospheric conditions, or to the proportion of the land surface that is covered by water (i.e., to wetting and drying).Supplementary data (original data, digitized version of the maps, metadata) are archived under PANGAEA (http://dx.doi.org/10.1594/PANGAEA.855124).

Climate change is an important control of carbon cycling, particularly in the Arctic. Permafrost degradation through deeper thaw and physical disturbances result in the release of carbon dioxide and methane to the atmosphere and to an increase in riverine dissolved organic matter (DOM) fluxes. Whereas riverine DOM fluxes of the large Arctic rivers are well assessed, knowledge is limited with regard to small catchments that cover more than 40 % of the Arctic drainage basin. Here, we use absorption measurements to characterize changes in DOM quantity and quality in a Low Arctic (Herschel Island, Yukon, Canada) and a High Arctic (Cape Bounty, Melville Island, Nunavut, Canada) setting with regard to geographical differences, impacts of permafrost degradation and rainfall events. We find that DOM quantity and quality is controlled by differences in vegetation cover and soil organic carbon content. The Low Arctic site has higher SOCC and greater abundance of plant material introducing higher lignin concentrations into the aquatic system and resulting in a stronger color of DOM than in the High Arctic. There is a strong relationship between dissolved organic carbon (DOC) concentration and absorption characteristics (cDOM) for surface waters at both sites similar to the one for the great Arctic rivers. We used the optical characteristics of DOM such as cDOM absorption, Specific UltraViolet Absorbance SUVA, UltraViolet UV Slope, Slope Ratio for assessing quality changes downstream, at baseflow and stormflow conditions and in relation to permafrost disturbance. DOM in streams at both sites demonstrated optical signatures indicative of photodegradation downstream processes, even over short distances of 2000 m. It was determined that flow pathways and the connected hydrological residence time control DOM quality. Deeper flow pathways allow the export of permafrost-derived DOM, whereas shallow pathways with shorter residence times lead to the export of fresh near-surface derived DOM. Compared to the large Arctic rivers, DOM quality exported from the small catchments studied here is much fresher and therefore prone to degradation. This work shows that optical properties of DOM will be a useful tool for understanding DOM sources and quality at a pan-Arctic scale.

Climate change is affecting the rate of carbon cycling, particularly in the Arctic. Permafrost degradation through deeper thaw and physical disturbances results in the release of carbon dioxide and methane to the atmosphere and to an increase in lateral dissolved organic matter (DOM) fluxes. Whereas riverine DOM fluxes of the large Arctic rivers are well assessed, knowledge is limited with regard to small catchments that cover more than 40 % of the Arctic drainage basin. Here, we use absorption measurements to characterize changes in DOM quantity and quality in a low Arctic (Herschel Island, Yukon, Canada) and a high Arctic (Cape Bounty, Melville Island, Nunavut, Canada) setting with regard to geographical differences, impacts of permafrost degradation, and rainfall events. We find that DOM quantity and quality is controlled by differences in vegetation cover and soil organic carbon content (SOCC). The low Arctic site has higher SOCC and greater abundance of plant material resulting in higher chromophoric dissolved organic matter (cDOM) and dissolved organic carbon (DOC) than in the high Arctic. DOC concentration and cDOM in surface waters at both sites show strong linear relationships similar to the one for the great Arctic rivers. We used the optical characteristics of DOM such as cDOM absorption, specific ultraviolet absorbance (SUVA), ultraviolet (UV) spectral slopes (S275–295), and slope ratio (SR) for assessing quality changes downstream, at base flow and storm flow conditions, and in relation to permafrost disturbance. DOM in streams at both sites demonstrated optical signatures indicative of photodegradation downstream processes, even over short distances of 2000 m. Flow pathways and the connected hydrological residence time control DOM quality. Deeper flow pathways allow the export of permafrost-derived DOM (i.e. from deeper in the active layer), whereas shallow pathways with shorter residence times lead to the export of fresh surface- and near-surface-derived DOM. Compared to the large Arctic rivers, DOM quality exported from the small catchments studied here is much fresher and therefore prone to degradation. Assessing optical properties of DOM and linking them to catchment properties will be a useful tool for understanding changing DOM fluxes and quality at a pan-Arctic scale.

River water is the main source of dissolved organic carbon (DOC) in the Arctic Ocean. DOC plays an important role in the Arctic carbon cycle, and its export from land to sea is expected to increase as ongoing climate change accelerates permafrost thaw. However, transport pathways and transformation of DOC in the land-to-ocean transition are mostly unknown. We collected DOC and aCDOM(λ) samples from 11 expeditions to river, coastal and offshore waters and present a new DOC–aCDOM(λ) model for the fluvial–marine transition zone in the Laptev Sea. The aCDOM(λ) characteristics revealed that the dissolved organic matter (DOM) in samples of this dataset are primarily of terrigenous origin. Observed changes in aCDOM(443) and its spectral slopes indicate that DOM is modified by microbial and photo-degradation. Ocean colour remote sensing (OCRS) provides the absorption coefficient of coloured dissolved organic matter (aCDOM(λ)sat) at λ=440 or 443 nm, which can be used to estimate DOC concentration at high temporal and spatial resolution over large regions. We tested the statistical performance of five OCRS algorithms and evaluated the plausibility of the spatial distribution of derived aCDOM(λ)sat. The OLCI (Sentinel-3 Ocean and Land Colour Instrument) neural network swarm (ONNS) algorithm showed the best performance compared to in situ aCDOM(440) (r2=0.72). Additionally, we found ONNS-derived aCDOM(440), in contrast to other algorithms, to be partly independent of sediment concentration, making ONNS the most suitable aCDOM(λ)sat algorithm for the Laptev Sea region. The DOC–aCDOM(λ) model was applied to ONNS-derived aCDOM(440), and retrieved DOC concentration maps showed moderate agreement to in situ data (r2=0.53). The in situ and satellite-retrieved data were offset by up to several days, which may partly explain the weak correlation for this dynamic region. Satellite-derived surface water DOC concentration maps from Medium Resolution Imaging Spectrometer (MERIS) satellite data demonstrate rapid removal of DOC within short time periods in coastal waters of the Laptev Sea, which is likely caused by physical mixing and different types of degradation processes. Using samples from all occurring water types leads to a more robust DOC–aCDOM(λ) model for the retrievals of DOC in Arctic shelf and river waters.

The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt., Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, 689

SOX11 is a key Transcription Factor (TF) in the regulation of embryonic and adult neurogenesis, whose mutation has recently been linked to an intellectual disability syndrome in humans. SOX11’s transient activity during neurogenesis is critical to ensure the precise execution of the neurogenic program. Here, we report that SOX11 displays differential subcellular localizations during the course of neurogenesis. Western-Blot analysis of embryonic mouse brain lysates indicated that SOX11 is post-translationally modified by phosphorylation. Using Mass Spectrometry, we found 10 serine residues in the SOX11 protein that are putatively phosphorylated. Systematic analysis of phospho-mutant SOX11 resulted in the identification of the S30 residue, whose phosphorylation promotes nuclear over cytoplasmic localization of SOX11. Collectively, these findings uncover phosphorylation as a novel layer of regulation of the intellectual disability gene Sox11.

In permafrost areas, seasonal freeze-thaw cycles result in upward and downward movements of the ground. For some permafrost areas, long-term downward movements were reported during the last decade. We measured seasonal and multi-year ground movements in a yedoma region of the Lena River Delta, Siberia, in 2013–2017, using reference rods installed deep in the permafrost. The seasonal subsidence was 1.7 ± 1.5 cm in the cold summer of 2013 and 4.8 ± 2 cm in the warm summer of 2014. Furthermore, we measured a pronounced multi-year net subsidence of 9.3 ± 5.7 cm from spring 2013 to the end of summer 2017. Importantly, we observed a high spatial variability of subsidence of up to 6 cm across a sub-meter horizontal scale. In summer 2013, we accompanied our field measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) on repeat-pass TerraSAR-X (TSX) data from the summer of 2013 to detect summer thaw subsidence over the same study area. Interferometry was strongly affected by a fast phase coherence loss, atmospheric artifacts, and possibly the choice of reference point. A cumulative ground movement map, built from a continuous interferogram stack, did not reveal a subsidence on the upland but showed a distinct subsidence of up to 2 cm in most of the thermokarst basins. There, the spatial pattern of DInSAR-measured subsidence corresponded well with relative surface wetness identified with the near infra-red band of a high-resolution optical image. Our study suggests that (i) although X-band SAR has serious limitations for ground movement monitoring in permafrost landscapes, it can provide valuable information for specific environments like thermokarst basins, and (ii) due to the high sub-pixel spatial variability of ground movements, a validation scheme needs to be developed and implemented for future DInSAR studies in permafrost environments.

Permafrost landscapes experience different disturbances and store large amounts of organic matter, which may become a source of greenhouse gases upon permafrost degradation. We analysed the influence of terrain and geomorphic disturbances (e.g. soil creep, active-layer detachment, gullying, thaw slumping, accumulation of fluvial deposits) on soil organic carbon (SOC) and total nitrogen (TN) storage using 11 permafrost cores from Herschel Island, western Canadian Arctic. Our results indicate a strong correlation between SOC storage and the topographic wetness index. Undisturbed sites stored the majority of SOC and TN in the upper 70 cm of soil. Sites characterised by mass wasting showed significant SOC depletion and soil compaction, whereas sites characterised by the accumulation of peat and fluvial deposits store SOC and TN along the whole core. We upscaled SOC and TN to estimate total stocks using the ecological units determined from vegetation composition, slope angle and the geomorphic disturbance regime. The ecological units were delineated with a supervised classification based on RapidEye multispectral satellite imagery and slope angle. Mean SOC and TN storage for the uppermost 1 m of soil on Herschel Island are 34.8 kg C m-2 and 3.4 kg N m-2, respectively. Copyright © 2015 John Wiley & Sons, Ltd.

Aims: An Arctic Vegetation Classification (AVC) is needed to address issues related to rapid Arctic-wide changes to climate, land-use, and biodiversity. Location: The 7.1 million km2 Arctic tundra biome. Approach and conclusions: The purpose, scope and conceptual framework for an Arctic Vegetation Archive (AVA) and Classification (AVC) were developed during numerous workshops starting in 1992. The AVA and AVC are modeled after the European vegetation archive (EVA) and classification (EVC). The AVA will use Turboveg for data management. The AVC will use a Braun-Blanquet (Br.-Bl.) classification approach. There are approximately 31,000 Arctic plots that could be included in the AVA. An Alaska AVA (AVA-AK, 24 datasets, 3026 plots) is a prototype for archives in other parts of the Arctic. The plan is to eventually merge data from other regions of the Arctic into a single Turboveg v3 database. We present the pros and cons of using the Br.-Bl. classification approach compared to the EcoVeg (US) and Biogeoclimatic Ecological Classification (Canada) approaches. The main advantages are that the Br.-Bl. approach already has been widely used in all regions of the Arctic, and many described, well-accepted vegetation classes have a pan-Arctic distribution. A crosswalk comparison of Dryas octopetala communities described according to the EcoVeg and the Braun-Blanquet approaches indicates that the non-parallel hierarchies of the two approaches make crosswalks difficult above the plantcommunity level. A preliminary Arctic prodromus contains a list of typical Arctic habitat types with associated described syntaxa from Europe, Greenland, western North America, and Alaska. Numerical clustering methods are used to provide an overview of the variability of habitat types across the range of datasets and to determine their relationship to previously described Braun-Blanquet syntaxa. We emphasize the need for continued maintenance of the Pan-Arctic Species List, and additional plot data to fully sample the variability across bioclimatic subzones, phytogeographic regions, and habitats in the Arctic. This will require standardized methods of plot-data collection, inclusion of physiogonomic information in the numeric analysis approaches to create formal definitions for vegetation units, and new methods of data sharing between the AVA and national vegetation- plot databases.

Arctic vegetation phenology is a sensitive indicator of a changing climate and rapid assessment of vegetation status is necessary to more comprehensively understand the impacts on foliar condition and photosynthetic activity. Airborne and space-borne optical remote sensing have been successfully used to monitor vegetation phenology in Arctic ecosystems by exploiting the biophysical and biochemical changes associated with vegetation growth and senescence. However, persistent cloud cover and low sun angles in the region make the acquisition of high quality temporal optical data within one growing season challenging. In the following study, we examine the capability of “near-field” remote sensing technologies, in this case digital, true colour, cameras to produce surrogate in-situ spectral data to characterize changes in vegetation driven by seasonal pigment dynamics. Simple linear regression was used to investigate relationships between common pigment-driven spectral indices calculated from field-based spectrometry and red, green, and blue (RGB) indices from corresponding digital photographs in three dominant vegetation communities across three major seasons at Toolik Lake, North Slope, Alaska. We chose the strongest and most consistent RGB index across all communities to represent each spectral index. Next, linear regressions were used to relate RGB indices and extracted leaf-level pigment content with a simple additive error propagation of the root mean square error (RMSE). Results indicate that the green-based RGB indices had the strongest relationship with chlorophyll a and total chlorophyll, while a red-based RGB index showed moderate relationships with the chlorophyll to carotenoid ratio. The results suggest that vegetation colour contributes strongly to the response of pigment-driven spectral indices and RGB data can act as a surrogate to track seasonal vegetation change associated with pigment development and degradation. Overall, we find that low-cost, easy-to-use digital cameras can monitor vegetation status and changes related to seasonal foliar condition and photosynthetic activity in three dominant, low-Arctic vegetation communities.

Satellite‐derived surface soil moisture data are available for the Arctic, but detailed validation is still lacking. Previous studies have shown low correlations between in situ and modeled data. It is hypothesized that soil temperature variations after soil thaw impact MetOp ASCAT satellite‐derived surface soil moisture (SSM) measurements in wet tundra environments, as C band backscatter is sensitive to changes in dielectric properties. We compare in situ measurements of water content within the active layer at four sites across the Arctic in Alaska (Barrow, Sagwon, Toolik) and Siberia (Tiksi), taken in the spring after thawing and in autumn prior to freezing. In addition to the long‐term measurement fields, where sensors are installed deeper in the ground, we designed a monitoring setup for measuring moisture very close to the surface in the Lena River Delta, Siberia. The volumetric water content (VWC) and soil temperature sensors were placed in the moss organic layer in order to account for the limited penetration depth of the radar signal. ASCAT SSM variations are generally very small, in line with the low variability of in situ VWC. Short‐term changes after complete thawing of the upper organic layer, however, seem to be mostly influenced by soil temperature. Correlations between SSM and in situ VWC are generally very low, or even negative. Mean standard deviation matching results in a comparably high root‐mean‐square error (on average 11%) for predictions of VWC. Further investigations and measurement networks are needed to clarify factors causing temporal variation of C band backscatter in tundra regions., Key Points Organic layer temperature variations explain temporal behavior of soil moisture derived from C band radar in unfrozen wet tundraTemporal variability of volumetric water content from in situ point measurements is representative over distances of several kilometers in tundraNear‐surface volumetric water content predicted from matched ASCAT data has an average RMSE of 11% across five sites in Alaska and Siberia

High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR ≦ GPP

Arctic warming is leading to substantial changes to permafrost including rapid Degradation of ice and ice-rich coasts and riverbanks. In this study, we present and evaluate a high spatiotemporal resolution three-year time series of X-Band microwave satellite data from the TerraSAR-X (TSX) satellite to quantify cliff-top erosion (CTE) of an ice-rich permafrost riverbank in the central Lena Delta. We apply a threshold on TSX backscatter images and automatically extract cliff-top lines to derive intra- and inter-annual CTE. In order to examine the drivers of erosion we statistically compare CTE with climatic baseline data using linear mixed models and analysis of variance (ANOVA). Our evaluation of TSX-derived CTE against annual optical-derived CTE and seasonal in situ measurements showed good agreement between all three datasets. We observed continuous erosion from June to September in 2014 and 2015 with no significant seasonality across the thawing season. We found the highest net annual cliff-top erosion of 6.9 m in 2014, in accordance with above-average mean temperatures and thawing degree days as well as low precipitation. We found high net annual erosion and erosion variability in 2015 associated with moderate mean temperatures but above average precipitation. According to linear mixed models, climate parameters alone could not explain intra-seasonal erosional patterns and additional factors such as ground ice content likely drive the observed erosion. Finally, mean backscatter intensity on the cliff surface decreased from -5.29 to -6.69 dB from 2013 to 2015, respectively, likely resulting from changes in surface geometry and properties that could be connected to partial slope stabilization. Overall, we conclude that X-Band backscatter time series can successfully be used to complement optical remote sensing and in situ monitoring of rapid tundra permafrost erosion at riverbanks and coasts by reliably providing information about intra-seasonal dynamics.

We analyzed chlorophyll-a and Colored Dissolved Organic Matter (CDOM) dynamics from field measurements and assessed the potential of multispectral satellite data for retrieving water-quality parameters in three small surface reservoirs in the Brazilian semiarid region. More specifically, this work is comprised of: (i) analysis of Chl-a and trophic dynamics; (ii) characterization of CDOM; (iii) estimation of Chl-a and CDOM from OLI/Landsat-8 and RapidEye imagery. The monitoring lasted 20 months within a multi-year drought, which contributed to water-quality deterioration. Chl-a and trophic state analysis showed a highly eutrophic status for the perennial reservoir during the entire study period, while the non-perennial reservoirs ranged from oligotrophic to eutrophic, with changes associated with the first events of the rainy season. CDOM characterization suggests that the perennial reservoir is mostly influenced by autochthonous sources, while allochthonous sources dominate the non-perennial ones. Spectral-group classification assigned the perennial reservoir as a CDOM-moderate and highly eutrophic reservoir, whereas the non-perennial ones were assigned as CDOM-rich and oligotrophic-dystrophic reservoirs. The remote sensing initiative was partially successful: the Chl-a was best modelled using RapidEye for the perennial one; whereas CDOM performed best with Landsat-8 for non-perennial reservoirs. This investigation showed potential for retrieving water quality parameters in dry areas with small reservoirs.

Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture. These attributes result in similar spectral characteristics between distinct vegetation communities. In this study we examine spectral variability at three phenological phases of leaf-out, maximum canopy, and senescence of ground-based spectroscopy, as well as a simulated Environmental Mapping and Analysis Program (EnMAP) and simulated Sentinel-2 reflectance spectra, from five dominant low-Arctic tundra vegetation communities in the Toolik Lake Research Area, Alaska, in order to inform spectral differentiation and subsequent vegetation classification at both the ground and satellite scale. We used the InStability Index (ISI), a ratio of between endmember and within endmember variability, to determine the most discriminative phenophase and wavelength regions for identification of each vegetation community. Our results show that the senescent phase was the most discriminative phenophase for the identification of the majority of communities when using both ground-based and simulated EnMAP reflectance spectra. Maximum canopy was the most discriminative phenophase for the majority of simulated Sentinel-2 reflectance data. As with previous ground-based spectral characterization of Alaskan low-Arctic tundra, the blue, red, and red-edge parts of the spectrum were most discriminative for all three reflectance datasets. Differences in vegetation colour driven by pigment dynamics appear to be the optimal areas of the spectrum for differentiation using high spectral resolution field spectroscopy and simulated hyperspectral EnMAP and multispectral Sentinel-2 reflectance spectra. The phenological aspect of this study highlights the potential exploitation of more extreme colour differences in vegetation observed during senescence when hyperspectral data is available. The results provide insight into both the community and seasonal dynamics of spectral variability to better understand and interpret currently used broadband vegetation indices and also for improved spectral unmixing of hyperspectral aerial and satellite data which is useful for a wide range of applications from fine-scale monitoring of shifting vegetation composition to the identification of vegetation vigor.

International audience; The MEthane Remote sensing Lidar missioN (MERLIN) aims at demonstrating the spaceborne active measurement of atmospheric methane, a potent greenhouse gas, based on an Integrated Path Differential Absorption (IPDA) nadir-viewing LIght Detecting and Ranging (Lidar) instrument. MERLIN is a joint French and German space mission, with a launch currently scheduled for the timeframe 2021/22. The German Space Agency (DLR) is responsible for the payload, while the platform (MYRIADE Evolutions product line) is developed by the French Space Agency (CNES). The main scientific objective of MERLIN is the delivery of weighted atmospheric columns of methane dry-air mole fractions for all latitudes throughout the year with systematic errors small enough (

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene represent the most common genetic cause of Parkinson's disease (PD). However, LRRK2 function and molecular mechanisms causing the parkinsonian phenotype remain widely unknown. Most of LRRK2 knockdown and overexpression models strengthen the relevance of LRRK2 in regulating neurite outgrowth. We have recently identified ARHGEF7 as the first guanine nucleotide exchange factor (GEF) of LRRK2. This GEF is influencing neurite outgrowth through regulation of actin polymerization. Here, we examined the expression profile of neuroblastoma cells with reduced LRRK2 and ARHGEF7 levels to identify additional partners of LRRK2 in this process. Tropomyosins (TPMs), and in particular TPM4, were the most interesting candidates next to other actin cytoskeleton regulating transcripts in this dataset. Subsequently, enhanced neurite branching was shown using primary hippocampal neurons of LRRK2 knockdown animals. Furthermore, we observed an enhanced number of growth cones per neuron and a mislocalization and dysregulation of ARHGEF7 and TPM4 in these neuronal compartments. Our results reveal a fascinating connection between the neurite outgrowth phenotype of LRRK2 models and the regulation of actin polymerization directing further investigations of LRRK2-related pathogenesis.